Exploring Factors that Contribute to Country Development
Education and the World: Literacy rates, Human Development Index, and their relationship
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“The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and having a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.” -UN Development Program
In the world map below, countries are colored according to their Human Development Index score. Each country is assigned an HDI score-a number between 0 and 1, designed, in a rough sense, to measure quality of life. Notice that countries further from the equator are more likely to have a high HDI score than countries closer to the equator. This trend shows up as a visual gradient on the map: the further from the equator, the higher the HDI score, the more blue the countries appear. But this is not a general rule. The term “Gloabl South” is often used to describe a collection of so-called “under-developed” countries near the equator and south of it, a collection which the map below suggests.
However, his map is quite one dimensional. Just what exactly does HDI tell us? What, in concrete terms, does “human development” mean? The goal of the following analysis is to shed light on HDI through other measures, in particular those related to expected years of education, literacy rates, population density, and women’s empowerment.
Education
HDI vs. Expected Years of Education
Education is widely considered to have a strong contribution to the advancements of societies and the quality of life of the people who live in them. In the definition of the HDI measurement mentioned above, being knowledgeable is a contributing factor to a country’s HDI value. Curious to see how the length of time spent in school affects the UN’s measure of knowledge and a country’s HDI, we plotted countries’ HDI vs expected years of schooling to observe the trend between the two.
## `geom_smooth()` using formula = 'y ~ x'
Note: The points that belong to the NA continent group are countries
that were unsuccessfully matched with a continent during the data
wrangling process.
The linear regression of the plotted points has an \(R^2\) of 0.802, supporting the assumption of a strong correlation between the two variables. However, there are clearly some other factors that must influence the HDI as we can see that Europe has more countries plotted over the regression while Africa has more countries plotted below the regression. Although we know the main factors that are used to measure HDI, these results point towards regional variables influencing of HDI (could be issues accessing resources, emigration, etc.). In the future, we’d be curious to explore what the issues common issues among the countries below the regression and common accomplishments among countries above the regression.
Chloropleth Comparison
Below, we’ve included chloropleths of the expected years of education and HDI so they can be compared side by side. These chloropleths help the countries that are plotted further from the regression stand out because their more drastic change in shade from one graph to the other compared to some of the other countries’ more nuanced changes.
Education
HDI
Literacy Rate and HDI
We begin our inquiry into HDI and education by asking: Which is a better predictor of literacy rates - HDI, or average number of years of education? More over, what does it mean if HDI predicts literacy rates better than average number of years of education?
For each country, we can find an expected number of years of schooling; this is the number of years the average student attends school. In countries where the average years of schooling is higher, we expect to find higher average literacy rates.
For each continent, we calculated two correlation coefficient. First, we found the correlation between HDI score and literacy rate; in other words, how well does HDI predict literacy rate for that continent. Second, we found the correlation between average years of education and literacy rate; in other words, how well does years of schooling predict literacy rate for that continent.
Next, for each continent, we found the difference between these two correlations. The interesting results are those where this difference is small. A small difference in these two values means that “development” is as good a predictor of literacy rates as years of education. A small difference indicates that non-educational “developmental” factors are influencing literacy rates.
## Warning in left_join(., Education_Literacy, Education_Literacy_cor, by = c(region = "region")): Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
## warning.
## Warning in left_join(., HDI_Literacy, HDI_Literacy_cor, by = c(region = "region")): Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
## warning.
Observe that two continents, South America and Africa, are picked out as
having a smaller difference. This means that in these two continents,
extra educational factors are influencing literacy rates. This
observation tracks with the delineation into “Global South” and “Global
North” indicated by the plot of HDI. That is, the literacy rates of
South America and Africa, continents situated in the Global South,
suffer from extra-educational factors.
One problem with this analysis is that it is not granular. It gives us a view of the world that is split into seven, when in reality, the world has far more than seven borders.
Our next analysis clusters countries according to literacy rate and population density. The goal of the analysis is to show that the division into Global North and Global South is inadequate to understand differences in literacy rates. In other words, the delineation into North and South indicated by HDI is a simplification - the actual situation is more complicated.
Before this analysis can proceed, we first make an observation about the relationship between population density and literacy rates. Compare the plots of Population Density vs. Literacy Rate, and Log of Population Density vs. Literacy Rate. Observe that a line of best fit on the first plot would be exponential, while in the second, a line of best fit would be linear. This suggests that for the purposes of clustering, it would be appropriate to cluster Log of Population Density against Literacy Rate.
The elbow plot shows that a cluster analysis using three clusters is most appropriate. The plot below associates each country with one of three clusters. The first cluster, 1, consists of countries with high literacy rate and low population density. The second cluster, 2, consists of countries with high literacy rate and high density. The third cluster, 3, consists of countries with low literacy rate. Notice that this third cluster ranges over a wide variety of population densities.
## # A tibble: 3 × 5
## latestRate_scaled density_scaled size withinss cluster
## <dbl> <dbl> <int> <dbl> <fct>
## 1 0.412 -1.07 47 25.9 1
## 2 -1.77 -0.150 33 42.1 2
## 3 0.432 0.612 90 54.4 3
The following map colors each country according to its cluster
assignment. What is interesting about this map is that it shows how
groups of contiguous countries are likely to fall into the same cluster.
What does this mean? As an example, examine the pair of North African
countries Algeria and Libya. These two countries are near the equator,
and in our previous analysis, were part of the group described as the
Global South. Here however, we can see that Algeria and Libya belong to
a collection of countries with high literacy rate.
Visualizations
Visualizations, particularly interactive ones, will be well-received. That said, do not overuse visualizations. You may be better off with one complicated but well-crafted visualization as opposed to many quick-and-dirty plots. Any plots should be well-thought-out, properly labeled, informative, and visually appealing.
If you want to include dynamic visualizations or tables, you should explore your options from packages that are built from htmlwidgets. These htmlwidgets-based packages offer ways to build lighterweight, dynamic visualizations or tables that don’t require an R server to run! A more complete list of packages is available on the linked website, but a short list includes:
- plotly: Interactive graphics with D3
- leaflet: Interactive maps with OpenStreetMap
- dygraphs: Interactive time series visualization
- visNetwork: Network graph visualization vis.js
- sparkline: Small inline charts
- threejs: Interactive 3D graphics
You may embed a published Shiny app in your blog if useful, but be aware that there is a limited window size for embedded objects, which tends to makes the user experience of the app worse relative to a dedicated Shiny app page. Additionally, Shiny apps will go idle after a few minutes and have to be reloaded by the user, which may also affect the user experience.
Any Shiny apps embedded in your blog should be accompanied by the link to the published Shiny app (I did this using a figure caption in the code chunk below, but you don’t have to incorporate the link in this way).
Tables
DT package
The DT package is great for making dynamic tables that can be displayed, searched, and filtered by the user without needing an R server or Shiny app!
Note: you should load any packages you use in the setup
code chunk as usual. The library() functions are shown
below just for demonstration.
library(DT)
mtcars %>%
select(mpg, cyl, hp) %>%
datatable(colnames = c("MPG", "Number of cylinders", "Horsepower"),
filter = 'top',
options = list(pageLength = 10, autoWidth = TRUE))kableExtra package
You can also use kableExtra for customizing HTML tables.
library(kableExtra)
summary(cars) %>%
kbl(col.names = c("Speed", "Distance"),
row.names = FALSE) %>%
kable_styling(bootstrap_options = "striped",
full_width = FALSE) %>%
row_spec(0, bold = TRUE) %>%
column_spec(1:2, width = "1.5in") | Speed | Distance |
|---|---|
| Min. : 4.0 | Min. : 2.00 |
| 1st Qu.:12.0 | 1st Qu.: 26.00 |
| Median :15.0 | Median : 36.00 |
| Mean :15.4 | Mean : 42.98 |
| 3rd Qu.:19.0 | 3rd Qu.: 56.00 |
| Max. :25.0 | Max. :120.00 |
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“Safe Space” by artist Kenesha Sneed
This is a figure caption
You may also use Markdown syntax for displaying images as shown below, but code chunks offer easier customization of the image size and alignment.
This is another figure caption
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Customizing your blog design
As a final detail only if you have time,
you can explore options for customizing the style of your blog. By
default, we are using the readthedown theme from the rmdformats
package (see Line 6 of this file if you want to switch out
themes).
Theme
You can use the rmdformats package to play around with some pre-built themes. There are, I’m sure, many many many more similar packages with built in themes, or you can look into how to include a CSS code chunk to customize aspects of a theme.
Using the rmdformats package, you can change the theme itself (Line 6):
rmdformats::readthedownrmdformats::downcute- For
downcuteonly, you can add a new indented line below Line 6 with the codedowncute_theme: "chaos"for thedowncute chaostheme
- For
rmdformats::robobookrmdformats::material
You can explore additional YAML options by looking at the rmdformats
package page or running, for example,
?rmdformats::readthedown() to see the help documentation
for a particular theme from the package.
Synax highlighting
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"default""tango""pygments""kate""monochrome""espresso""zenburn""haddock""textmate"NULLfor no syntax highlighting (not recommended)
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References
All data sources, any key R packages, and any other sources used in developing your blog should be cited in full in a list of references at the end of your blog. Your blog post should also link to these sources as they are discussed. You may choose any reference style as long as sources are fully cited (try to be consistent!).
Typically, references in R Markdown (and LaTeX) files are incorporated with a BibTeX database (a .bib file). You can try this approach or manually include either a numbered or alphabetized list.
Columbia University has compiled some guidance on how to cite data. Some data sources will give you the citation information to copy and paste. Use the provided citations or citation styles in those cases.
You can list R package citations with the code
citation("packageName") in the console and then copy (and
reformat as needed) the relevant text, e.g.,
##
## To cite package 'DT' in publications use:
##
## Xie Y, Cheng J, Tan X (2023). _DT: A Wrapper of the JavaScript
## Library 'DataTables'_. R package version 0.27,
## <https://CRAN.R-project.org/package=DT>.
##
## A BibTeX entry for LaTeX users is
##
## @Manual{,
## title = {DT: A Wrapper of the JavaScript Library 'DataTables'},
## author = {Yihui Xie and Joe Cheng and Xianying Tan},
## year = {2023},
## note = {R package version 0.27},
## url = {https://CRAN.R-project.org/package=DT},
## }
The following citations are based on the American Statistical Association citation style (not all of these references are used in this document).:
Baumer, B. S., Kaplan, D. T., and Horton, N. J. (2021), Modern Data Science with R (2nd ed.), Boca Raton, FL: CRC Press.
Broman, K. W. and Woo, K. H. (2018), “Data Organization in Spreadsheets,” The American Statistician, 72:1, 2-10, doi: 10.1080/00031305.2017.1375989
Columbia University Libraries (n.d.), “Data Citation,” available at https://guides.library.columbia.edu/datacitation.
McNamara, A. and Horton N. J. (2018) “Wrangling Categorical Data in R,” The American Statistician, 72:1, 97-104, doi: 10.1080/00031305.2017.1356375.
Shah, Syed A. A. (October 2022), “Starbucks Drinks” (Version 1), Kaggle, available at https://www.kaggle.com/datasets/syedasimalishah/starbucks-drinks.
Xie Y, Cheng J, Tan X (2022). “DT: A Wrapper of the JavaScript Library ‘DataTables’,” R package version 0.24, available at https://CRAN.R-project.org/package=DT.
Breaking Barriers: Key Factors for Measuring Women’s Progress Across Countries
Why wont HDI suffice?
Amartya Sen, a Nobel laureate and renowned economist, once quoted, “empowering women is the key to building the future we want.” This simple yet powerful statement highlights the significance of gender equality and its impact on human development. The notion of human development is rooted in the idea of expanding people’s choices, enabling them to fulfill their potential, and giving them the freedom to lead lives they value. However, the reality is that women’s choices and freedoms are not equally and they continue to be marginalized across the globe.
While countries with higher HDI ranks are generally associated with greater levels of freedom and empowerment, the reality is more complex. For instance, a country’s overall HDI score may mask significant disparities in gender inequality within its population. In many countries, women continue to face discrimination in areas such as education, employment, and political representation, despite their nation’s high HDI ranking. Moreover, the cultural and social norms prevalent in a country can significantly impact the empowerment of women, even in countries with high HDI scores. Therefore, while HDI rankings can provide a broad measure of a country’s level of human development, it is crucial to examine specific indicators that measure the empowerment of women to gain a more nuanced understanding of gender inequality across the globe.
To gain a better understanding of the complex issues that women face worldwide, we will analyze standardized indicators of women’s empowerment across countries based on their population and HDI rank. This analysis will reveal the factors that contribute to gender inequality and highlight areas for improvement to advance women’s empowerment and create a more equitable society.
Indicators of Women’s Empowerment
We will look into four key indicators of Women’s Empowement measure
Adolescent Birth Rate: This metric measures the number of births per 1,000 women between the ages of 15 and 19 in a given year. A high adolescent birth rate is often an indicator of poor sexual and reproductive health outcomes for young women, and can also be a barrier to educational and economic opportunities.
Political Participation: This metric measures the extent to which women are involved in political decision-making processes, including representation in elected offices, participation in political parties, and involvement in civil society organizations. Women’s political participation is important for ensuring that their voices and perspectives are heard in policy-making processes.
Labor Participation: This metric measures the percentage of women who are employed or seeking employment in the labor force. A low labor force participation rate can be an indicator of limited economic opportunities for women, which can in turn contribute to poverty and economic inequality.
Secondary Level Education: The women’s indicator of secondary level education is a metric that measures the percentage of women in a given population who have completed secondary education. This indicator is often used as a measure of women’s educational attainment and their access to educational opportunities.
Heatmap: HDI, Polulation, Key indicators of Women’s Empowerment
This heatmap represents data on different indicators of women’s empowerment across 30 most popullous countries, ranked according to their HDI (highest HDI rank in the top and lowest in the bottom). Each row and column of the heatmap represents a different country and a specific indicator, respectively. The colors in the heatmap represent different values of each indicator, with the lighter shades indicating lower values and the darker shades indicating higher values. The values are standardized to make the intepretations easier. For Example, darker colors indicate that a particular country is doing better on that indicator compared to countries with lighter colors. Additionally, dendrograms are included at the top and left sides of the heatmap, which show how countries and indicators are clustered together based on similarities in their values.
Key Interpretations
The heatmap shows that there is generally a negative correlation between adolescent birthrate and HDI ranking, meaning that countries with higher development tend to have lower adolescent birthrates. However, the heatmap shows that it is not always the case. Some of the exceptions are Uganda and Nigeria, which have high adolescent birthrates despite their moderate development levels. Conversely, some countries with lower HDI rankings, such as India and Algeria, have lower adolescent birthrates
The heatmap reveals that women’s political participation is not consistently correlated with a country’s HDI ranking. Contrary to the common belief that higher HDI ranking equates to greater political participation for women, the data shows that this is not always the case. For instance, countries like Mexico, with a lower HDI ranking, exhibit higher women’s political participation compared to Japan, which has a higher HDI ranking but a lower participation rate. This suggests that factors other than development, such as cultural and social norms, may play a role in determining women’s political participation. Therefore, a more nuanced and context-specific approach is necessary to understand the complex interplay between development and women’s political participation.
The heatmap shows a strong positive correlation between women’s labor force participation and HDI ranking. Countries with higher HDI rankings tend to have higher labor force participation rates for women, while those with lower HDI rankings tend to have lower participation rates. However, it is worth noting that there are still significant disparities in women’s labor force participation rates within and across countries, even among those with high HDI rankings.
The education level column of the heatmap, specifically reflecting the metric of women’s completion of secondary education, shows a positive correlation with HDI. In general, countries with higher HDI rankings tend to have higher rates of women completing secondary education, indicating greater access to educational opportunities and greater potential for personal and professional growth. However, there are exceptions to this trend. One such exception is Ethiopia, which ranks relatively high in the HDI spectrum but has very low rates of women completing secondary education. This indicates that while Ethiopia has made progress in areas such as healthcare and income, it may face challenges in ensuring equal access to education for women.